Official websites use. Share sensitive information only on official, secure websites. Correspondence: Dr. Email: anna. Together with other large-scale biobank and genetic association studies of complex traits, these GWAS of kidney functionβrelated traits have also provided novel insight into the relationship of kidney function to other diseases with respect to their genetic associations, genetic correlation, and directional relationships.
In this review article, we will summarize these recent GWAS of CKD and kidney functionβrelated traits, explain approaches for downstream characterization of associated genetic loci and the value of such computational follow-up analyses, and discuss related challenges along with potential solutions to ultimately enable improved treatment and prevention of kidney diseases through genetics.
The genetic contribution to both kidney function in the healthy range and to kidney diseases is supported by significant heritability estimates and a long line of familial aggregation and linkage studies 1 , 2. GWAS is a mapping method for identifying genetic variants associated with an outcome across the genome in an unbiased manner. It tests for a statistical association between genotype at a genetic markerβtypically a single nucleotide polymorphism SNP βand the outcome, typically a human trait or disease.
By performing this test for millions of SNPs genome-wide, this study design holds the promise of uncovering biologic mechanisms related to the outcome through identifying the genes and variants that drive association signals with the outcome. When the genes or variants causing the association are amenable to modulation, they may represent potential therapeutic targets.
GWAS and their meta-analyses are commonly used for locus discovery of complex diseases and traits using data from largely population-based cohorts.
